The Nature of the Nuisance—Damage or Threat—Determines How Perceived Monetary Costs and Cultural Benefits Influence Farmer Tolerance of Wildlife
Bibliographic record
Abstract
Biodiversity-friendly farming is a growing area of discussion among farmers, as well as in government departments and non-government organizations interested in conservation on private land. Those seeking to encourage biodiversity on farms must understand the production challenges presented by wildlife. Such species destroy agricultural commodities or present threats to family, pets, or infrastructure. A survey of farmers in the Canadian Maritime provinces sought to understand the drivers of tolerance. Our results demonstrated that estimated monetary losses from a species were largely unrelated to the perceived acceptability of those losses. Rather, the type of nuisance—damage to crops/property or threat to the safety of people, pets, or livestock—determined whether a loss would be perceived as acceptable and if that acceptability would influence tolerance. For damaging species, the perception of cultural benefits seemed able to convert high estimated economic losses to acceptable ones, for overall tolerance. For threatening species, however, minor perceived financial losses seemed augmented by low perceived benefits and made unacceptable, leading to intolerance. Female, older, and part-time farmers were most likely to identify threatening species as a nuisance. The use of an elicitation-based survey design provided novel insight as a result of the lack of prompts, but also presented analytical challenges that weakened predictive power. Recommendations are given for further research and management.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".